Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generation

• URL: http://arxiv.org/abs/2310.05119v1
• Date: Sun, 8 Oct 2023 11:20:02 GMT
• Title: Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generation
• Authors: Weihua Liu, Youyuan Xue, Chaochao Lin, Said Boumaraf
• Abstract summary: We propose a Dynamic Multi-Domain Knowledge(DMDK) network for radiology diagnostic report generation. The DMDK network consists of four modules: Chest Feature Extractor(CFE), Dynamic Knowledge Extractor(DKE), Specific Knowledge Extractor(SKE), and Multi-knowledge Integrator(MKI) module. We performed extensive experiments on two widely used datasets, IU X-Ray and MIMIC-CXR.
• Score: 0.5939858158928474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automated generation of radiology diagnostic reports helps radiologists make timely and accurate diagnostic decisions while also enhancing clinical diagnostic efficiency. However, the significant imbalance in the distribution of data between normal and abnormal samples (including visual and textual biases) poses significant challenges for a data-driven task like automatically generating diagnostic radiology reports. Therefore, we propose a Dynamic Multi-Domain Knowledge(DMDK) network for radiology diagnostic report generation. The DMDK network consists of four modules: Chest Feature Extractor(CFE), Dynamic Knowledge Extractor(DKE), Specific Knowledge Extractor(SKE), and Multi-knowledge Integrator(MKI) module. Specifically, the CFE module is primarily responsible for extracting the unprocessed visual medical features of the images. The DKE module is responsible for extracting dynamic disease topic labels from the retrieved radiology diagnostic reports. We then fuse the dynamic disease topic labels with the original visual features of the images to highlight the abnormal regions in the original visual features to alleviate the visual data bias problem. The SKE module expands upon the conventional static knowledge graph to mitigate textual data biases and amplify the interpretability capabilities of the model via domain-specific dynamic knowledge graphs. The MKI distills all the knowledge and generates the final diagnostic radiology report. We performed extensive experiments on two widely used datasets, IU X-Ray and MIMIC-CXR. The experimental results demonstrate the effectiveness of our method, with all evaluation metrics outperforming previous state-of-the-art models.

Related papers

• Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach [5.9091823080038814]
  Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data.
  arXiv  Detail & Related papers  (2024-08-29T08:26:00Z)
• HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling [4.44283662576491]
  We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods.
  arXiv  Detail & Related papers  (2024-03-20T05:50:04Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• Cross-Modal Causal Intervention for Medical Report Generation [109.83549148448469]
  Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance. Due to the spurious correlations within image-text data induced by visual and linguistic biases, it is challenging to generate accurate reports reliably describing lesion areas. We propose a novel Visual-Linguistic Causal Intervention (VLCI) framework for MRG, which consists of a visual deconfounding module (VDM) and a linguistic deconfounding module (LDM)
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Self adaptive global-local feature enhancement for radiology report generation [10.958641951927817]
  We propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report. Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR) Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information. Finally, the captioning generator generates the radiology reports through multi-granularity features.
  arXiv  Detail & Related papers  (2022-11-21T11:50:42Z)
• Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation [7.118069629513661]
  We introduce a novel fined-grained knowledge graph structure called an attributed abnormality graph (ATAG) The ATAG consists of interconnected abnormality nodes and attribute nodes, allowing it to better capture the abnormality details. We show that the proposed ATAG-based deep model outperforms the SOTA methods by a large margin and can improve the clinical accuracy of the generated reports.
  arXiv  Detail & Related papers  (2022-07-04T05:32:00Z)
• AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation [50.21065317817769]
  We propose an AlignTransformer framework, which includes the Align Hierarchical Attention (AHA) and the Multi-Grained Transformer (MGT) modules. Experiments on the public IU-Xray and MIMIC-CXR datasets show that the AlignTransformer can achieve results competitive with state-of-the-art methods on the two datasets.
  arXiv  Detail & Related papers  (2022-03-18T13:43:53Z)
• Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation [55.00308939833555]
  The PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD) PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias. PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
  arXiv  Detail & Related papers  (2021-06-13T11:10:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.